Structured learning involves learning and making inferences from inputs that can be both unstructured (e.g., text) and structured (e.g., graphs and graph fragments), and making predictions about outputs that are also structured as graphs. Examples include the construction of knowledge bases from noisy extraction data, inferring temporal event graphs from newswire or social media, and inferring influence structure on social graphs from multiple sources.One of the challenges of this setting is that it often does not fit into the classic supervised or unsupervised learning paradigm. In essence, we have one large (potentially infinite) partially observed input graph, and we are trying to make inferences about the unknown aspects of this graph's structure. Often there is side information available, which can be used for enrichment, but in order to use this information, we need to infer mappings for schema and ontologies that describe that side information, perform alignment and entity resolution, and reason about the added utility of the additional sources. The topic is extremely pressing, as many of the modern challenges in extracting usable knowledge from (big) data fall into this setting. Our focus in this workshop is on the machine learning and inference methods that are useful in such settings.